Summary of Efficacy Of Synthetic Data As a Benchmark, by Gaurav Maheshwari et al.
Efficacy of Synthetic Data as a Benchmark
by Gaurav Maheshwari, Dmitry Ivanov, Kevin El Haddad
First submitted to arxiv on: 18 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this research paper, the authors explore the representativeness of synthetic datasets generated using large language models (LLMs) for training and testing various natural language processing (NLP) tasks. By evaluating the effectiveness of these synthetic datasets across six different datasets and three tasks, including intent classification and named entity recognition, the study reveals that while they can accurately capture performance on simpler tasks, they fall short for more complex ones. The authors also propose a new metric called the bias factor to assess the biases introduced when using the same LLM to generate benchmarking data and perform tasks. Their findings suggest that smaller LLMs exhibit biases towards their own generated data, while larger models do not. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study shows how synthetic datasets can be used for training and testing NLP tasks like intent classification and named entity recognition. The authors test these datasets on six different datasets and three tasks to see how well they work. They find that simpler tasks are fine, but more complex tasks don’t work as well. The researchers also come up with a new way to measure the biases in synthetic data called the bias factor. |
Keywords
» Artificial intelligence » Classification » Named entity recognition » Natural language processing » Nlp » Synthetic data
